Article excerpt

Agricultural production estimates have often differentiated and estimated different technolo- gies within a sample of farms. The common approach is to use observable farm characteristics to split the sample into groups and subsequently estimate different functions for each group. Alternatively, unique technologies can be determined by econometric procedures such as la- tent class models. This paper compares the results of a latent class model with the use of a priori information to split the sample using dairy farm data. Latent class separation appears to be a superior method of separating heterogeneous technologies and suggests that technology differences are multifaceted.

The estimation of production (and cost or profit) functions usually relies on the assumption that the underlying technology is the same for all produc- ers. However, it is possible that technological het- erogeneity exists among farms, which means that some farms in an industry use different technolo- gies. In such a case, estimating a common tech- nology to all farms is not appropriate because it can yield biased estimates of the technological characteristics.

The issue of technological heterogeneity is of enormous relevance in studies of agricultural pro- duction when an agricultural sector is believed to be characterized by different technologies. Man- agement advice or policy implications may differ for these different sub-groups. For this reason, studies often control for the possibility of techno- logical heterogeneity, traditionally accomplished by selecting a major characteristic of the produc- tion process, dividing the sample based on this characteristic, and subsequently estimating differ- ent functions for each group. Some characteristics that have been used in agricultural studies are type of seed or variety planted (Xu and Jeffrey 1998, Balcombe et al. 2007), land type (Fuwa, Ed- monds, and Banik 2007), location (Battese, Mal- ik, and Broca 1993), or full-time versus part-time farms (Bagi 1984).

Technological heterogeneity is also believed to be present in dairy farming where different pro- duction systems may be utilized. Thus, in dairy empirical analysis it is essential to correctly iden- tify the groups of farms that operate under differ- ent technologies. Separating a sample of dairy farms into several groups and subsequently esti- mating separate functions was done by Hoch (1962), who split a sample of Minnesota dairy farms into two groups based on location; Bravo- Ureta (1986), who classified a sample of New Eng- land dairy farms based on the breed of the herd; Tauer (1998), who estimated different cost curves for stanchion and parlor dairy farms; Newman and Matthews (2006), who estimated different out- put distance functions for specialist and non-spe- cialist dairy farms; Brümmer, Glauben, and Thys- sen (2002), who estimated separate stochastic dairy production distance functions for three Euro- pean Countries; and Moreira and Bravo-Ureta (2010), who estimated different production func- tions for three Southern Cone countries to subse- quently estimate meta-technology ratios.

However, the use of a single or even multiple characteristics probably serves as an incomplete proxy to characterize a technology, since these characteristics may not exhaust all technology differences that exist between farms. Milking or feeding systems usually vary across dairy farms and may be an important descriptor of the tech- nology, but there are additional unobserved (not measured) factors that may reflect technology differences. For example, one of these unobserved factors could be the genetic potential of the dairy herd.

Rather than use prior separators, different tech- nologies within a sample can be isolated using statistical procedures. Groups of farms can be delineated using either cluster algorithms (Alva- rez et al. …